Cancellations of elective cases on the day of surgery waste valuable operating-room time. The authors studied cancellations at an American hospital and a Norwegian university hospital to test (a) whether the quality of hospital administrative data on cancellations is sufficient for meaningful comparative analysis and (b) whether causes of cancellations at these 2 major academic hospitals are comparable. Large retrospective cause-of-cancellation data sets were obtained from each hospital. The authors then prospectively established root causes of cancellations by on-site investigation and interviews of the hospital personnel involved. The surgical department at the Norwegian hospital cancelled 14.58% of cases in 2003 and 16.07% in 2004. The American hospital cancelled 16.52% of all cases between May 1, 2003, and April 30, 2004. Administrative data may give a rough picture of causes of cancellations. However, most findings at either of the hospitals do not translate easily to the other.
Statistical process control is useful for detecting changes in perioperative system performance, represented in this study by nonoperative time. The technique is able to detect changes quickly and to detect small changes over time.
There was a sudden, rather than a gradual, reduction of operative time leading to extra cases being performed. This coincided with (1) the surgeon being assigned a new fellow and (2) administrative commitment to finish three cases per day. Our original hypothesis was negated, but other controllable causes for changes in surgical throughput were identified.
ObjectiveTo inform the design of IT support, the authors explored the characteristics and sources of process variability in a surgical care process that transcends multiple institutions and professional boundaries.SettingA case study of the care process in the Abdominal Aortic Aneurysm surveillance programme of three hospitals in Norway.DesignObservational study of encounters between patients and surgeons accompanied by semistructured interviews of patients and key health personnel.ResultsFour process variety dimensions were identified. The captured process variations were further classified into intended and unintended variations according to the cause of the variations. Our main findings, however, suggest that the care process is best understood as systematised analysis and mitigation of risk. Even if major variations accommodated for the flexibility needed to achieve particular clinical aims and/or to satisfy patient preferences, other variations reflected healthcare actors' responses to risks arising from a lack of resilience in the existing system. On this basis, the authors outlined suggestions for a resilience-based approach by including awareness in workflow as well as feedback loops for adaptive learning. The authors suggest that IT process support should be designed to prevent process breakdowns with patient dropouts as well as to sustain risk-mitigating performance.ConclusionProcess variation was in part induced by systemised risk mitigation. IT-based process support for monitoring processes such as that studied here should aim to ensure resilience and further mitigate risk to enhance patient safety.
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